Geoduck Clam (Panopea Abrupta) Demographics and Mortality Rates inthe Presence of Sea Otters (Enhydra Lutris) and Commercial Harvesting
Bibliographic record
Abstract
In British Columbia, expanding sea otter (Enhydra lutris) populations are creating concerns among commercial harvesters about the potential predation impacts on exploitable geoduck clam (Panopea abrupta) stocks. We analysed fishery-independent surveys of exploited geoduck clam populations along a gradient of sea otter occupancy on the west coast of Vancouver Island, British Columbia, Canada to assess relationships between otter presence, commercial fishery removals of geoduck, and geoduck population demographics. Geoduck mean density, age composition, and estimated total mortality were influenced by a combination of variables, and therefore, we could not differentiate among geoduck populations according to sea otter presence or absence alone. As expected, we found a strong association between commercial fishery removals and geoduck clam total mortality rates. In contrast, the local numbers of sea otters were not an important factor affecting geoduck total mortality. A more balanced study design and greater sampling intensity would increase the power to detect whether sea otter predation affects harvestable geoduck stocks. Also, knowledge of the consumption rate by sea otters of geoduck throughout the year, in combination with survey data of unfished geoduck populations, would facilitate better prediction of how geoduck clam mortality rates might change as sea otters re-colonise new areas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".